GPU accelerated real-time confocal fluorescence lifetime imaging microscopy (FLIM) based on the analog mean-delay (AMD) method

Abstract

We demonstrated GPU accelerated real-time confocal fluorescence lifetime imaging microscopy (FLIM) based on the analog mean-delay (AMD) method. Our algorithm was verified for various fluorescence lifetimes and photon numbers. The GPU processing time was faster than the physical scanning time for images up to 800 × 800, and more than 149 times faster than a single core CPU. The frame rate of our system was demonstrated to be 13 fps for a 200 × 200 pixel image when observing maize vascular tissue. This system can be utilized for observing dynamic biological reactions, medical diagnosis, and real-time industrial inspection.

Simple and Robust Deep Learning Approach for Fast Fluorescence Lifetime Imaging

“…Graphics processing units (GPUs) have been extensively adopted as hardware accelerators for FLIM. Byungyeon et al [17] proposed a mean-delay technique and implemented it on a GPU to directly extract fluorescence lifetime from analogue fluorescent decays. Additionally, a GPU was utilized to realize a pixel-wise, fitting-free phasor method [18] towards a two-photon FLIM system to achieve videorate fluorescence lifetime inference.…”

Fast fluorescence lifetime imaging techniques: A review on challenge and development

“…By using a graphics processor unit (GPU) for image buildup, the image rate was increased to 13 fps for 200 Â 200-pixel images. 124 Theoretically, analog recording is free of \pileup" and counting loss e®ects. Thus, there is no intrinsic limitation in photon rate or signal intensity.…”

High-accuracy and video-rate lifetime extraction from time correlated single photon counting data on a graphical processing unit

“…While the use of GPUs to speed up FLIM has been proposed in recent work, 27 the approach builds on a different analog mean-delay method that could not capture double-exponential decaying, as we do.…”